Development of a Digital Twin to Analyse Waiting Lists in a Sleep‐Disordered Breathing Unit

This study developed and validated a digital twin of patient flow and waiting list dynamics in a Sleep‐Disordered Breathing Unit, based on a discrete‐event simulation (DES) model in the context of growing diagnostic demand mainly driven by obstructive sleep apnoea. The stochastic model, implemented in MATLAB, reproduces six care stages (e‐Consultation, face‐to‐face consultation, overnight pulse oximetry, respiratory polygraphy, polysomnography and follow‐up consultation), using operational data from year 2024 for parameterisation and data from year 2025 for correlation and validation. Key performance indicators included mean waiting time and mean queue length per stage, with model fidelity assessed via Mean Absolute Error, Root Mean Squared Error and Relative Percent Difference (RPD), both by stage and globally across the full patient pathway. The model accurately reproduced the aggregate system workload, with a weighted RPD of 3.6% for mean queue length and 34.6% for mean waiting time. Respiratory polygraphy showed the best agreement in terms of service load (RPD 2.7%), whereas the follow‐up consultation exhibited the largest discrepancies in waiting times (RPD 73.7%), likely related to prioritisation rules and organisational variability not explicitly modelled. Overall, this digital twin provides an operationally useful representation to support ‘what‐if’ analyses of organisational scenarios aimed at reducing diagnostic delays, representing one of the first validated applications of DES model in a dedicated Sleep‐Disordered Breathing Unit and provides a basis for future applications incorporating urgency stratification and prospective analysis.